Mortality inequalities measured by socioeconomic indicators in Brazil: a scoping review

ABSTRACT OBJECTIVE Summarize the literature on the relationship between composite socioeconomic indicators and mortality in different geographical areas of Brazil. METHODS This scoping review included articles published between January 1, 2000, and August 31, 2020, retrieved by means of a bibliographic search carried out in the Medline, Scopus, Web of Science, and Lilacs databases. Studies reporting on the association between composite socioeconomic indicators and all-cause, or specific cause of death in any age group in different geographical areas were selected. The review summarized the measures constructed, their associations with the outcomes, and potential study limitations. RESULTS Of the 77 full texts that met the inclusion criteria, the study reviewed 24. The area level of composite socioeconomic indicators analyzed comprised municipalities (n = 6), districts (n = 5), census tracts (n = 4), state (n = 2), country (n = 2), and other areas (n = 5). Six studies used composite socioeconomic indicators such as the Human Development Index, Gross Domestic Product, and the Gini Index; the remaining 18 papers created their own socioeconomic measures based on sociodemographic and health indicators. Socioeconomic status was inversely associated with higher rates of all-cause mortality, external cause mortality, suicide, homicide, fetal and infant mortality, respiratory and circulatory diseases, stroke, infectious and parasitic diseases, malnutrition, gastroenteritis, and oropharyngeal cancer. Higher mortality rates due to colorectal cancer, leukemia, a general group of neoplasms, traffic accident, and suicide, in turn, were observed in less deprived areas and/or those with more significant socioeconomic development. Underreporting of death and differences in mortality coverage in Brazilian areas were cited as the main limitation. CONCLUSIONS Studies analyzed mortality inequalities in different geographical areas by means of composite socioeconomic indicators, showing that the association directions vary according to the mortality outcome. But studies on all-cause mortality and at the census tract level remain scarce. The results may guide the development of new composite socioeconomic indicators for use in mortality inequality analysis.


INTRODUCTION
Observed within different sociodemographic groups [1][2][3] , the inverse relationship between low socioeconomic status and mortality is a well-established fact in the literature and has mostly been analyzed by single-variable socioeconomic indicators such as income, education, wealth, race/ethnicity, marital status, social class, and occupation 4,5 . Composite socioeconomic indicators such as the Human Development Index, deprivation scores, and social-vulnerability indexes [6][7][8] have also been used to study mortality inequalities in populations. These more complex measures broaden the knowledge on socioeconomic disparities, especially in analyses that consider different geographical levels, such as municipalities, or other small areas.
In Brazil, several studies provide evidence of higher mortality rates in more impoverished areas [9][10][11] . Many are the composite socioeconomic measures available at the municipal level, such as the Social Vulnerability Index (Índice de Vulnerabilidade Social -IVS) 12 , and the Municipal Human Development Index (MHDI) 13 -still, mortality rates can be highly heterogeneous 14 , making more disaggregated analyses desirable. Smaller spatial units like districts or census tracts (which include districts), however, often lack socioeconomic information 15 , resulting in few studies on mortality inequality at this level of analysis.
Using indicators to analyze mortality inequalities at different geographical levels has been most beneficial for researchers and health policy makers to identify the risks of death in population groups and to define public policies and interventions 16,17 . Mapping the construction of composite socioeconomic indicators, and their association with mortality outcomes at different geographical levels in Brazil, is of paramount importance to guide the development of new composite indicators and their use in studies analyzing mortality inequalities. As such, this study summarizes the literature on the relationship between composite socioeconomic indicators and mortality in different geographical areas of Brazil.

Specific Research Questions
To do so, we formulated the following research questions: Research question 1: Which composite socioeconomic indicators are most used to understand mortality inequalities across different Brazilian geographical areas?
Research question 2: What are the characteristics of these composite measures of area-level socioeconomic indicators, and are there any limitations to understanding geographical mortality inequalities in Brazil?

METHODS
This scoping review follows the Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines and is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Statement extension for Scoping Reviews (PRISMA-ScR) 18 . Its protocol was submitted and published on the Open Science Framework (OSF) (https://osf.io/vmt9f/).
We used the population, concept, and context framework to define our research question 19 . Population was defined as individuals who had died in Brazil, considering all-cause, or specific causes of death in any age range; concept was understood as the aggregate measures of socioeconomic position; and the context was the geographical level in Brazil (i.e., state, municipality, census tract level, districts, and others) 18 .

Eligibility Criteria
This scoping review included papers that: i) were published in peer-reviewed journals between January 1, 2000 and August 31, 2020; ii) had cross-sectional, cohort, case-control, and ecological study designs; iii) analyzed the relationship (i.e., association or descriptive relationship) between socioeconomic status and all-cause, cause-specific, or prevalence mortality rates; iv) outcomes for the general population could be provided by administrative or primary data, without age group or geographical area level restriction. Articles that exclusively accounted for single measures of the socioeconomic condition, reviews, trials, intervention studies, editorials, comments, and case reports were excluded.

Outcomes
Primary outcomes consisted of all-cause mortality, while secondary outcomes comprised cause-specific mortality -both defined according to the International Classification of Diseases (ICD). Data were processed from baseline to follow-up. If a study reported multiple follow-ups, only the most recent data was included.

Information Sources and Search Strategy
We performed a bibliographic search on August 31, 2020 An adapted version of this search strategy was drafted and used for the Web of Science, Scopus, and Lilacs databases. Final search results were exported into EndNote, and two blinded authors removed any duplicates. All references were managed in EndNote X7. We did not search for gray literature.

Selection of Evidence Sources and Data Charting
Three pairs of reviewers independently evaluated the titles, abstracts, and full texts of the selected articles. Prior to standardized data extraction, the reviewers were trained on key study descriptors to harmonize the extraction: i) article identification (language, authors, year, and journal of publication); ii) composite socioeconomic measure (name, data source, variables used, level of analysis, and geographical coverage); iii) mortality outcomes (cause of death, age group, data sources, and type of measure); iv) statistical analysis; and v) main findings. Disagreements between reviewers were resolved by means of discussion, and in collaboration with a third reviewer as necessary. We did not estimate the agreement rate for the reviews. Finally, two pairs of reviewers verified all the previously extracted information. Information was summarized in tables and boxes.

Summary of Results
Data analysis was carried out following the narrative summary approach 20 . Results were tabulated considering the publication year, geographical coverage, and mortality outcomes of the study. The summary included: the different socioeconomic indicators available, the all-cause and specific cause of death, and the main findings and limitations -as well as critical points the authors failed to address -of the selected studies. Information was summarized according to the population coverage of the socioeconomic measure, area level, composition and scale of the socioeconomic inequality measures incorporated, data and information sources used, and analytic methods used to describe the relationship between socioeconomic inequalities and mortality outcomes.

RESULTS
We retrieved a total of 806 papers -of which we removed 208 duplicates and excluded other 521 following title screening, leaving a total of 77 full-text articles for assessment. Figure describes the exclusion process during the full-text review. Most studies were excluded for not including a composite socioeconomic measure (n = 33), or not being peer-reviewed articles (n = 12). After screening, 24 articles remained for the scoping review.

Included
Eligibility Screening Box 1. Summary of the selected studies according to socioeconomic inequities and mortality and main findings. All-cause, cause-specific, and age-adjusted (> 60 years old) mortality rate Positive correlation between social deprivation and cause-specific deaths. Districts with extreme social deprivation had a 2.9 times higher risk of death due to traffic accidents, and 3.9 times higher risk of pneumonia in older adults.  (1997) All-cause homicide mortality rate and by type of weapon, gender, race/skin color, age, and areas of social exclusion/ inclusion The gradient of homicide mortality rates increased as the degree of social exclusion increased. There was a very sharp decline in homicide mortality rates in extreme and high exclusion (-79.3% and -71.7%, respectively).
There was also a decline in the mean and degree of social exclusion (-59.1% and -61.9%, respectively). The distribution of rate terciles at the area level highlighted a spatial gradient of mortality in the city. In the poorer areas (second and third terciles), higher mortality rates prevailed.

Continue
Box 1 presents the selected articles organized according to composite socioeconomic measures and mortality outcomes. Some studies assessed mortality outcomes at the small area level (census tract) [21][22][23][24] , making it difficult to generalize their results for the whole of Brazil, since the combined composite socioeconomic measure was only constructed for a given geographical area (Box 1).
Most articles (n = 16) elaborated composite socioeconomic indicators using Brazilian Demographic Census data: two, ten, and four articles, respectively, were written using data from the 1991 25 Four studies used global indicators as measures, such as the Human Development Index (HDI), which considers variables related to income, education, and longevity 23,[37][38][39] ; and the Gross Domestic Product (GDP), i.e., the sum of all final goods and services produced in a given period of time 28 . One study 40 45 . Only one study did not specify the source of mortality data used 30 .
The source of population count data used as the denominator for the mortality rates was either the Brazilian Institute of Geography and Statistics (Instituto Brasileiro de Geografia e Estatística -IBGE) censuses or the Live Birth Information System (Sistema de Informação sobre Nascidos Vivos -SINASC) (Box 1).
Mortality rates were mostly presented in non-standardized form 24,30,32,37,45 , and commonly calculated for a specific age group, such as infant mortality [34][35][36]42 and mortality of older adults 29 (Box 1). One study evaluated life expectancy 33 . To tackle the different frequency distributions in diverse populations, some authors chose age-standardized rates 25,27,40,43,46 , or stratification of rates by different age groups and other population characteristics, such as sex and race/ethnicity 21,23,30,38,39,44 . The studies either estimated the rates for each year or measured the average mortality rate between periods. They also used proportional mortality linked to causes or age groups 25,42 . Cause-specific mortality rates comprised external causes 22 Higher mortality rates due to colorectal cancer 40 , leukemia 27 , a general group of neoplasms 29 , traffic accidents 32, 45 , and suicide 32 , in turn, were observed in less deprived areas and/or those with more significant socioeconomic development. Medeiros et al. 28 (2012) showed that the variables in the socioeconomic development measure only partially explained the differences in mortality rates due to cardiovascular diseases in a group of socioeconomically similar municipalities, being more strongly associated with other determinants.

Author(s)/ Year Limitations
Peres et al. 21 (2011) Lack of temporal data on the potential social determinants of homicide decline made it impossible to infer its causes. There were no discussions on the limitations resulting from the social exclusion/inclusion index.

Vilela et al. (2008) 22
The limitations considered were intra-aggregate heterogeneity, inter-group mobility, and the underreporting of infant deaths.

Antunes et al. (2008) 23
Different ways of measuring variables in statistical censuses in Barcelona and São Paulo. As an ecological study, it does not consider the relevant variation in individual socioeconomic characteristics. Another limitation is the relatively simple analytical scheme, which disregards non-linear relationships between mortality and socioeconomic status.

Silveira and Junger 24 (2018)
Use of secondary data is a limiting factor in this study, as is the possibility of ecological studies.
Schuck-Paim et al. 38 (2019) Despite the synthetic control method used to detect the benefits of pneumococcal conjugate vaccine introduction and explicitly designed to minimize confounding, the ecological study design may have disregarded other uncontrolled factors that can affect the estimates.
Medeiros et al 28 (2012) Use of secondary data is a limiting factor in this study. Information may not be completely reliable and represents population averages as it is an ecological study.
Ribeiro et al. 27 (2007) Ecological study that had to consider "ecological fallacy." No individual assessment of socioeconomic status was performed in the study, and the smallest unit analyzed (state) was too large to represent a neighborhood effect.
Silva et al. 29 (2008) The associations found may be stronger due to spatial aggregation (neighborhood). The districts of Recife still have significant social heterogeneity, with wealth areas existing alongside pockets of poverty. Moreover, this study characterized mortality using an indicator created by other authors. Research shows that synthetic indicators do not capture the different nuances of social reality.
Oliveira et al. 43 (2010) Considering the nature of the aggregate measure, a neighborhood classified with the highest deprivation does not always have the worst rates on all variables analyzed. The high variation in population composition (between 2,500 and 60,000 people) across districts was not considered. Regarding statistical analysis, no mortality smoothing techniques was performed, as it was not possible to assess the effect of deprivation on mortality. Use of the 2000 census to obtain socioeconomic indicators may result in limitations in understanding previous years.

Belon and Barros 33 (2011)
As a unit of analysis and area of residence, a limitation of this study is that its results do not necessarily reflect the situation of those belonging to each socioeconomic stratum.
Macedo et al. 26 (2001) The stratification adopted in the study, although performed by aggregating similar zones, has several limitations due to the particular heterogeneity of the urban area of Salvador. Problems related to the quality of information were also studied.

Bastos et al. 32 (2009)
An important limitation of ecological studies is that the relationship between two variables does not necessarily reflect the situation under study. Administrative regions may have caused degrees of heterogeneity due to the specific characteristics of each neighborhood.
Faria and Santana (2016) 35 Use of secondary data can be considered a limiting factor in this study. Araújo et al. 45 (2005) Lack of data on living conditions disaggregated by neighborhood prevented the generation of a weighted indicator for classification according to more specific sociodemographic variables.
Moreover, the quality of violent death records restricted a more comprehensive understanding.
Alves et al. 41 (2020) Limiting factors comprise the use of secondary data and the fact that deaths due to more severe forms of the outcome were not verified.
Guimarães et al. 40 An ecological design that needed to measure the variables as proxies: income does not directly interfere with colorectal cancer. It can promote conditions to decrease exposure to risk factors, such as diet (primary prevention), and establish early diagnosis (secondary prevention).
Alarcão et al. 39 (2020) Use of secondary data and collinear variables (schooling, income, and employment), which may impair the strength of the association, are limitations in this study.
Bonfim et al. 36 (2020) Given the difference in coverage of the Mortality Information System throughout Brazil, the use of secondary data is a possible limitation in this study.
Studies found higher mortality risks for tuberculosis (RR = 2.9) 41 , pneumonia (RR = 3.9) 29 , cardiovascular diseases (RR = 3.3) 30 , cerebrovascular diseases (RR = 3.9) 30 , stroke (OR = 2.0) 45 , homicide (RR = 5.1) 26 , traffic accidents (RR = 2.9) 29 , and infectious and parasitic diseases among children (RR = 1.48) 22 in more deprived areas compared with less deprived areas. But no statistically significant association was found between mortality rates and indicator measures, such as the Composite Social Deprivation Indicator (Indicador Composto de Carência Social -ICS) 29 , the socioeconomic composition of districts 43 and the Composite Deprivation Index 43 (Box 2). Moreover, a study evaluating life expectancy at birth showed that this variable was 6.9 and 5.5 years less, respectively, for men and women living in impoverished areas, compared with those living in less deprived areas 33 .

Limitations Discussed by the Studies
Studies based on the ecological approach [22][23][24]27,28,32,33,38,40,43 reported some disadvantages regarding the assessment of mortality inequalities using composite socioeconomic measures (Box 2). As these studies were not designed to find an association between socioeconomic factors and mortality at the individual level 32,47,48 , and the potential explanation pointed to a decrease in heterogeneous spatial contexts, particularly in large areas and populations 22,26,32,33,43 , their results may not necessarily reflect the situation of individuals belonging to each socioeconomic strata (Box 2). Other limitations concerned the use of secondary data, even if from official governmental sources, which could mask underreporting of death, and the difference in SIM coverage between the different areas studied 28,[35][36][37]39,41 . As for analytical and measurement strategies, the studies discussed limitations in the availability of mortality data in censuses 36,37,41 , the difficulty of using rate smoothing methods in smaller areas 44 and more robust methods to assess the association between mortality and the composite socioeconomic measures used 23,39 (Box 2).

Study Limitations Noted by this Review
Some of the studies reviewed did not discuss possible study limitations, as described above 21,25,30,34,42,44 . Other important limitations also went unaddressed, such as the presence of a garbage code -i.e., causes of deaths that should not be considered underlying causes of deaths 49 -, and ill-defined causes of death (IDCD), which could influence the results when correction and distribution, respectively, are not performed 49 . We must also point out the lack of discussion regarding the uncertainty of mortality data in some Brazilian regions (north and northeast) and at small area levels, such as the census tract. The quality of the mortality information system also varies across these regions and could be a source of bias and therefore should be discussed. Since the composite socioeconomic measures used in mortality iniquity studies also vary, these should be addressed as the differences in definitions and concepts (i.e., deprivation, vulnerability, socioeconomic status, and poverty) could influence the interpretation of their findings.

DISCUSSION
To our knowledge, this is the first study to provide a comprehensive overview of the available literature on composite socioeconomic measures and mortality in different geographical areas of Brazil, and to identify the methodological challenges in analyzing these associations. Our main findings reveal that while some of the composite socioeconomic measures used in mortality studies covered the entire country, they were limited by the area of analysis -the municipality. Only four studies used the census tract as the small area level to assess mortality data, but their results were restricted to specific municipalities [21][22][23][24] . Cause-specific mortality outcomes (i.e., external causes, chronic and degenerative diseases, infectious and parasitic diseases) were the most frequent.
Most studies used descriptive and spatial analysis to estimate the relationship between socioeconomic measures and mortality outcomes, with a few articles employing regression analysis to estimate this association. None of the studies reviewed used a gradient analysis to estimate the aforementioned relationship. Some articles presented a gradient analysis according to socioeconomic status, where the lowest socioeconomic status had the highest mortality rates and the greatest increase in some mortality outcomes, or specific causes of death, as observed in other countries 6,8 . But we also found studies citing lower mortality rates in the lower socioeconomic strata, particularly for cancers 21,27,37,38 .
Currently in Brazil, we have a variety of socioeconomic indices that are construed based on different socioeconomic and geographical variables, and with different concepts. Thus, none of the development or vulnerability indicators, or similar concepts are available nationally for the entire country at different geographic levels 15 . Besides, current measures address concepts other than socioeconomic conditions. Although deprivation, poverty, and vulnerability broadly refer to a person's impoverished state compared to society as a whole, they are theoretically distinct. Vulnerability refers to the risk of experiencing a decline in well-being, or in the quality of living conditions. Similarly, material deprivation can be defined as lack of income, and other resources 50 . Poverty, in turn, is measured by alternative concepts based on subsistence, basic needs, and relative deprivation 51 .
Socioeconomic measures are popular and widely used in studies focused on assessing health outcomes and economic and social development results [1][2][3] . In Brazil, however, we have a lack of studies using standardized measures covering the entire country, as well as those related to all-cause mortality -since most of the studies reviewed here used cause-specific mortality. Since the distribution of all-cause and cause-specific mortality rates is a key metric for assessing population health, a better understanding of the impact of lower socioeconomic conditions on different levels and mortality trends can help policymakers plan and develop priorities for allocating health resources 52 .
In Brazil, the register of deaths is compulsory and such records are reported in national information systems, such as the Ministry of Health's Mortality Information System (SIM) and the Civil Registry Statistics System (RC). Moreover, the last IBGE Demographic Census, carried out in 2010, gathered information on deaths for the entire population of Brazil included in it 53 . Deaths in Brazil require certification by a physician, and are defined according to ICD codes 54 .
Despite great advances in recent decades in the quality of mortality information systems in Brazil, we still have significant underreporting of deaths, especially in less-developed regions of the north and northeast, added to the differences by sex, age, and area of residence 55,56 .
In small areas, the issue of significant data uncertainty regarding the number of deaths makes mortality estimates even more innacurate 14 . Consequently, studies that use mortality indicators without correcting for underreporting may not effectively measure mortality rates in the region and instead report false and misleading associations. Similarly, the last decade saw a reduction in the percentage of garbage codes in the mortality information system, which demonstrates its improved quality 57 . Also, after inclusion of the IDCD reclassification results in the country's official statistics published in 2010, the percentage of IDCD decreased from 8.6% to 7.0% among reported deaths. Such percentage, however, is still relatively high, presenting significant disparities between states and regions. This variation also occurs intra-regionally, with IDCD percentages close to 30.0% in some states' macro-regions 54 . In 2015, for example, studies observed an estimated 97.2% of deaths recorded in the mortality system 31 . Despite improvements in the quality and integrity of the SIM database over time, we still find heterogeneity in the frequency and completeness of reports 57,58 . Moreover, underestimation and mis-coding of deaths is more problematic in older adults and young children groups 31,59 .
All-cause and cause-specific mortality analyses should therefore be carried out using methods that consider correction for deaths by the remaining IDCD. Since the magnitude of these causes can be affected, this can introduce biases in comparisons between locations with different IDCD percentages, and between different socioeconomic groups. Due to issues with information quality, analyses of trends and leading causes of mortality in many lowand middle-income countries, such as Brazil, are usually restricted to areas with a higher socioeconomic status or larger cities; while places with the poorest quality of information on deaths have the heaviest disease burden. Such an issue requires further exploration in new studies to better understand the relationship between inequalities and mortality rates across the country 54 .
Death distribution reflects the countries' socioeconomic development contexts 60,61 .
Historical data from developed countries show that as their socioeconomic and health conditions improved, mortality rates tended to consistently decrease 60,61 -trend that has yet to become homogeneous for middle-and low-income countries, which possess substantial regional differences 60,61 . People of low socioeconomic status, defined by their per capita and/or household income, schooling, employment status, type of household, and internal and associated conditions, etc., are more likely to die younger than those of high socioeconomic status 62 .
Low socioeconomic status is consistently associated with an increased risk of premature and all-cause mortality. The reviewed studies show that the worst all-cause and causespecific mortality outcomes were associated with the worst socioeconomic measures. The mechanisms by which this social status can negatively affect health are diverse and include difficulty purchasing food, inadequate housing/neighborhoods, and barriers to accessing health and social services. Other social determinants may also explain these findings, such as: alcohol and tobacco consumption; different cultural standards related to healthy and unhealthy behavior; stress and low self-esteem associated with low socioeconomic status, which can lead to harmful physiological changes; less social capital in impoverished communities; and environmental factors (i.e., high crime/ violence rates, lack of public transportation, polluted roads, fast food outlets, and waste disposal sites 4,63,64 ). Regarding difficulties in accessing health services, studies report issues with prenatal care and early childhood care services, control of infectious diseases, and lack of access to dental services. They also point to the existence of social selection, a form of reverse causality in which disease causes, or deepens, social inequalities 65 .
Despite consistent reference to low socioeconomic status as a predictor for mortality, the aggregate scale of socioeconomic inequity on mortality in small areas in Brazil is still unclear. Existing socioeconomic measures only estimate social and economic inequalities down to the municipal level for the entire country 12, 13 . And those few measures available for disaggregated levels (i.e., census sector) are usually restricted to a single municipality or state. When evaluating a municipality, a better general socioeconomic condition may thus mask smaller pockets of extreme poverty. At the census tract level, the socioeconomic deprivation measure can identify areas with higher and lower mortality risks within the same municipality. Ultimately, identifying small areas with the worst mortality outcomes can guide the reallocation of resources and implementation of public policies.

Strengths and Limitations
To our knowledge, the present study is the first to review the literature on the relationship between composite socioeconomic indicators and mortality outcomes at different geographic levels in Brazil, and to identify the methodological challenges in analyzing these associations. Since we used a standard data extraction form for each paper included in the scoping review, our data should be as robust and standardized as possible. As the evidence reviewed may have been limited by the variety of terms/concepts related to composite socioeconomic measures such as deprivation, vulnerability, poverty, and socioeconomic status, our study also has limitations. Nevertheless, we consider that our search strategy, and the databases searched, included the main scientific literature on this topic. Our scoping review did not require a full risk of bias as it was not designed to produce an estimate of the effect of inequality on mortality. Instead, we summarized the limitations discussed by each study, highlighting any possible limitation that could influence the findings and was not reported.

CONCLUSIONS
This scoping review showed that studies have found higher rates, or higher percentages of increased mortality rates, in areas considered to be more impoverished, vulnerable, or have less socioeconomic development -despite remaining methodological omissions in measuring mortality disparities at lower geographic levels. Area-based deprivation indicators can facilitate linking information for socioeconomic and health conditions in the same area. The possibility of using a concise deprivation measure available for the lowest geographic level (census tract) across the country is essential for assessing health outcomes and for implementing public policies to reduce mortality inequalities in Brazil. Area-based deprivation indicators can also contribute to monitoring progress against the Sustainable Development Goal targets for different health outcomes.